Productivity Growth and Job Creation
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Transcript of Productivity Growth and Job Creation
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BACKGROUND PAPER FOR THE
WORLD DEVELOPMENT REPORT 2013
Productivity Growth and Job
Creation in the Development
Process of Industrial Clusters
Tetsushi Sonobe
Yuki Higuchi
Keijiro Otsuka
National Graduate Institute for Policy Studies, Japan
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Abstract
Poor management has long been suspected as a major constraint on job creation in the
manufacturing sector in low-income countries. In this sector, countless micro and small
enterprises in industrial clusters account for a large share of employment. This paper
examines the roles of industrial clusters, managerial capacities, and entrepreneurship inimproving productivity and creating jobs, by reviewing the literature and case studies,
including recent experiments. We find that managerial capacities are major determinants
of firms employment sizes and productivity growth, and that it is high innovative
capacities, accompanied by high managerial capacities, that boost cluster-based industrial
development.
JEL No. O12, O31, O32, M20, M50
Keywords: job creation, labor productivity, industrial cluster, management,
entrepreneurship.
The findings, interpretations, and conclusions expressed in this paper are entirely those of the
authors. They do not necessarily represent the views of the World Development Report 2013
team, the World Bank and its affiliated organizations, or those of the Executive Directors of the
World Bank or the governments they represent.
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Introduction
A large share of manufacturing employment, including the self-employed, in low income
countries is accounted for by industrial clusters, namely, agglomerations of firms
producing similar products or providing similar services in small geographical areas.
This is because there are economic forces making it more profitable for firms to operate inan industrial cluster than in isolation. Such economic forces are termed localization
economies after Marshalls (1920, Book IV, Ch. X) pioneering work on the localization of
industry. What will contribute to the betterment of wage workers, own-account workers,
managers, and entrepreneurs in these clusters? What can be done to strengthen the ability
of the clusters to create jobs? To answer these questions, we need a deeper understanding
of the nature and the limitation of localization economies.
Krugman (1991, Ch. 2) lists familiar examples of localization in the United States,
including Silicon Valley, Route 128, and North Carolinas Research Triangle as high tech
centers, Hartford as an insurance city, Chicago as the center of futures trading, and Los
Angeles as the entertainment capital. Even casual observers, however, know that a largenumber of industrial clusters both inside and outside the United States look quite different
from these familiar examples. In developing countries, there are rapidly growing
clusters, declining clusters, and traditional and still active clusters, but the majority are
what Altenburg and Mayer-Stamer (1999, p.1695) call survival clusters of micro and
small-scale enterprises which produce low-quality consumer goods for local markets.
While almost all clusters were formed spontaneously due to localization economies, their
performances vary considerably in growth, productivity, product quality, profitability,
employment sizes, and wage levels.
In an attempt to identify the reason for such considerable variance, this paper focuses on
the issues of how innovative and managerial capacities interact with localizationeconomies and diseconomies and what impacts they exert on the productivity and
employment of industrial clusters. We assume that entrepreneurship consists of
innovative capacity to put new ideas into effect and managerial capacity to improve
management efficiency given the level of technology. Our focus on managerial
capacities is motivated by our observation that they are in short supply in industrial
clusters in low income countries, especially in Sub-Saharan Africa. As is well-known,
localization facilitates knowledge spillovers, but this property is not beneficial for the
development of clusters when there are no new ideas about profitable products, new
markets, or new production processes to be spilt over or to be imitated in the cluster.
Localization does not encourage innovation or technology borrowing (i.e., learning from
abroad), but rather dampens them by facilitating rampant spillovers or imitation. Whilelocalization may attract diverse human resources, such as skilled workers, engineers, and
traders in one place, entrepreneurs may be unable to find a new useful combination of
these resources if their management capacities are inadequate. There is no reason to
assume that localization automatically nurtures entrepreneurship. Likewise, localization
does not nurture managerial capacities to execute plans aimed at management
improvement effectively.
This is not to say that industrial clusters are useless, however. On the contrary, industrial
clusters provide numerous benefits for firms and their workers within the clusters. For
example, the localization of industry saves on the cost of building infrastructure, enhances
the development of markets for skilled labor, and attracts buyers and material suppliers.
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It also reduces transaction costs that arise from information asymmetry and imperfect
contract enforcement. With low transaction costs, the division and specialization of labor
among firms are promoted and the provision of trade credits is facilitated in industrial
clusters. Of course, there are localization diseconomies as well. For example,
infrastructure may be overused resulting in serious congestion problems. Yet the fact that
a large number of firms are located in clusters indicates that localization economiesoutweigh localization diseconomies. The question is how to nurture entrepreneurship,
including managerial capacities, while taking full advantage of localization economies in
existing and new industrial clusters without aggravating localization diseconomies.
We use a simple model of micro and small enterprises (MSEs) in an industrial cluster to
understand why managerial capacities impact on the clusters capacity for job creation.
In the model, the major task of management is to maintain control of output, quality,
delivery, and costs, as emphasized by Deming (1982) and Toyotas Production System.
Entrepreneurship has an element of initiative, imagination, or innovative capacity (Baumol,
Schilling, and Wolff, 2009). Although its manifestation can have a variety of forms, the
development of an industrial cluster is boosted by similar or almost the same set ofinnovations in different industries in different countries, according to case studies
compiled by Schmitz and Nadvi (1999) and Sonobe and Otsuka (2006, 2011). Two
hypotheses emerge from these discussions. First, in stagnant clusters, firms have almost
equally small employment sizes, their labor productivity fluctuates wildly, and, therefore,
in a cross section of firms, no clear association is found between labor productivity and
employment sizes. Second, in dynamically growing clusters, employment size varies
among firms, labor productivity has small variances particularly among large firms, and
there is a positive association between labor productivity and employment sizes. The
main purpose of this study is to demonstrate that main difference between stagnant and
dynamic clusters can be attributed to the difference in management and innovative
capacities of entrepreneurs.
The empirical part of this paper uses enterprise data collected in industrial clusters in
Ghana, Ethiopia, Tanzania, Bangladesh, Vietnam, and China, with quite different
characteristics and growth performances. The data indicate that firmsemployment sizes
are small if production fluctuates wildly, a reflection of bad management. The data
provide suggestive evidence for the hypothesis that employment size is not closely
associated with labor productivity in the absence of innovations. By contrast, in the
clusters that have experienced innovations, employment sizes have grown rapidly and are
associated positively with labor productivity.
The rest of this paper is organized as follows. The next section briefly reviews the factorsassociated with productivity, including localization economies and diseconomies. In
Section 3, we develop a model highlighting the impact of managerial capacities on labor
employment. In Section 4, we discuss entrepreneurship and innovations in industrial
clusters. Toward the end of this section, we will develop hypotheses. Section 5
documents the empirical findings based on the selected case studies. Section 6 discusses
policy implications and concludes the paper.
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Localization economies and diseconomies
According to Mokyr (2005, pp. 1116 - 1117), there is a growing recognition among
economic historians that technological change was less important than institutional
changes in explaining episodes of economic growth before the Industrial Revolution.
The presence of peace, law and order, improved communication and trust . . .enforceable and secure property rights, and similar institutional improvements reduce
transaction costs and, hence, enable agents to specialize according to their comparative
advantage and to take advantage of economies of scale. Such commercial progress,
sometimes referred to as Smithian Growth, can be more important than technological
progress or Schumpeterian Growth.1
Smithian Growth is commonly found probably in all industrial clusters. Becker and
Murphy (1992) argue that transaction costs or coordination costs due to adverse selection,
moral hazard, and imperfect enforcement are generally low in industrial clusters where
transacting parties are located near each other. Low transaction costs facilitate the
division of labor among manufacturers and traders, which, as Marshall (1920) mentions,enables manufacturers to use specialized machinery at high utilization rates. Moreover,
the division of labor enables manufacturers to procure materials and parts flexibly and to
specialize in a narrow range of the production process, which saves both working capital
and fixed capital (Ruan and Zhang, 2009). The community mechanism found in
industrial clusters differs from the one found in a traditional village community that closes
its doors to outsiders and is counterproductive to the expansion of business networks
(Babur and Sonobe, 2012). The pseudo community mechanism that is intentionally used
to reduce transaction costs and to survive market competition accepts the entry of outsiders
and is suitable to expansion (Hayami, 2009).
In low-income countries, not just transaction costs arising from information andenforcement problems but also physical transport costs are high due to the shortage of
infrastructure (Eifert, Gelb, and Ramachandran, 2008). Industrial clusters or the
localization of industry can be viewed as a grassroots countermeasure to this problem
because being located nearby each other saves on the use of infrastructure. Other virtues
of localization economies in relation to Smithian Growthinclude the clustersability to
facilitate matching between job seekers with special skills and employers, which was
pointed out by Marshall (1920), and the clustersability to pull in more customers without
paying for advertising. Both reduce search costs. In addition, industrial clusters
facilitate knowledge spillovers, as was also pointed out by Marshall (1920), so that new
ideas of business spread quickly within clusters.
Syversons (2011) list of the determinants of productivity at the firm or plant level has two
broad categories: factors operating within the plant or firm, and external drivers of
productivity differences. The latter consists of productivity spillovers, competition,
deregulation or proper regulation, and flexible input markets. Note that the localization
of industry activates three of these four external drivers if a flexible input market is akin to
the developed division of labor among firms. This is the reason why most industries that
have developed spontaneously are cluster-based.
1 The concepts of Smithian Growth and Schumpeterian Growth were pioneered by Parker (1984).
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Industrial clusters, however, have no advantage in the category of internal drivers of
productivity differences, which include managerial practice/talent, higher-quality general
labor and capital inputs, information technology and R&D, learning by doing, product
innovation, and firm structure designs, according to Syverson (2011). Moreover, the
localization of industry may be counterproductive to some of these drivers. For example,
managerial talents and higher-quality labor of a firm may be poached by other firms in thesame cluster because of spillovers of information on talents and skills. Similarly, the
output of R&D and product innovation of a firm may be quickly imitated by other firms in
the same cluster before the innovator reaps profits, which will discourage innovative
activities. These are examples of localization diseconomies. Among other examples of
localization diseconomies is the congestion problem, which arises from the inadequate
provision of infrastructure.
Notwithstanding localization diseconomies, industrial clusters can have multifaceted
innovations in products, production processes, marketing, material procurements, and the
organizational design of firms, as the compilation of case studies of cluster-based
industrial development in Latin America and East Asia by Schmitz (1999) and Sonobe andOtsuka (2006) attests. According to these case studies, an industry in developing
countries begins by producing a low-quality imitation of an imported product. It is
initially difficult for the pioneering producer to produce and market the product because of
the lack of appropriate materials and because both the product and the producer are
unknown to potential buyers. Once these difficulties are overcome, however, the pioneer
earns high profits because of the absence of competitors. Observing the high profits,
there will be followers, including the former workers of the pioneer, who faithfully imitate
the pioneers production and marketing methods. This initiation process of an industry
may be viewed as Schumpeterian Growth, but it is short-lived and followed by the
Smithian Growththat is characterized by the formation of a cluster by the massive entry
of imitators, who seldom improve products and production processes, and the developmentof the division and specialization of labor among enterprises. Thus, Sonobe and Otsuka
(2006) refer to this process of cluster formation as quantity expansion.
The new entry of imitators will continue as long as they expect positive profits. Unless a
new market is developed, increases in the supply of their product to the local market will
eventually lower their product price and hence the profitability of continuing to produce
this product. A comparative study in Asia and Africa by Sonobe and Otsuka (2011) finds
that most clusters in Sub-Saharan Africa have reached or are reaching their long-run
equilibrium with zero profit. Such clusters are nothing but survival clusters of MSEs,
to use the terminology of Altenburg and Mayer-Stamer (1999). Many clusters in this
region have not yet had even an indication that Schumpeterian Growthis on the horizon,
whereas many clusters in East Asia have experienced such growth.
Managerial Capacities
Although management performs diverse functions, we focus on one of the basic functions;
that is, to maintain control of quality, output, delivery, and costs. We use a simple model
to illustrate how this function is related to the firms and the industrial clusters capacity
for productivity growth and job creation. Suppose that each firm in a cluster produces a
single product by using a technology characterized by a production function, F(L), where
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L is labor input and is a positive constant. The function is assumed to be increasing,
concave, and twice differentiable, i.e., F'(L) > 0, F''(L) < 0. While function Fgives the
expected output corresponding to inputL, actual outputxmay fluctuate so thatx= F(L),
where is a random variable with mean and variance 2. The profit is given by =
pF(L) wL, wherepis the product price and wis the wage rate, which the MSE takes as
given. We assume that is determined primarily by technology, whereas 2 isdetermined by management. The major results of the following analysis, however,
remains unchanged even if better management not only reduces 2by reducing the effects
of unforeseen undesirable events but also increases to some extent, or if the introduction
of a more productive but complicated new technology increases both and 2.
Fluctuation or variation in output arises from human fallibility and unforeseen events. It
is a result of accidents, errors, or mistakes, such as machine failures, workers injuries,
spoiled materials, the delayed supply of materials and parts, and the use of wrong parts.
These accidents, errors, or mistakes may be attributed partly to inadequate production
plans and product designs and partly to the lack of work standards or the established waythe workers do their jobs. In addition, there may be the ebb and flow of morale among
workers. When serious accidents occur simultaneously and when morale among workers
is low, actual outputxwill be smaller than F(L) and can be negative if we interpret pxas
value added (roughly equal to sales minus material cost). For example, a large part of theoutput may be rejected by the buyers on the grounds that the product quality is substandard.
On the other hand, the actual output can be larger than F(L), when a positive shock hits
the firm and work morale is high.
In what follows, we attempt to explore the optimum behaviors of firms under the two
settings; when the decision maker is risk averse and when firm must incur cost of over-
and under-production.
Risk aversion
The fluctuation is harmful when the decision maker is risk-averse. Consider a
risk-averse MSE owner, who maximizes expected utility E[U( )], where U is a concave
utility function. It is well-known that if U is an exponential utility function and is
distributed normally, this utility maximization is equivalent to
Max E( )V( ), (1)
where is the constant coefficient of absolute risk aversion and V( ) is the variance of .Althoughpmay be a random variable, we assume for a while that is the only source of
risk. Thus, equation (1) can be rewritten
Max pF(L)wL[pF(L)]22. (2)
The first-order condition is
pF'(L*)p2F(L*)2F'(L*) = w. (3)
This result is depicted in Figure 1. Under the assumption of the interior optimum, the
employment size that maximizes the expected utility, L
*
, is given by point Eat which the
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downward-sloping curve representing the left-hand side of equation (3) cuts the horizontal
line that shows the wage rate w. The variance, together with risk aversion, makes the
downward-sloping curve located below the mean marginal product of labor curvepF'(L)
and steep particularly for small values of L, thereby limiting the firms employment
capacity to a low level.
In the presence of risk aversion, what is the consequence of the fluctuation for job
creation? It is clear from equation (3) and Figure 1 that individual firms labor
employment,L, decreases as variance 2increases. It is also clear from equation (2) that
the certainty equivalent profit also decreases as 2increases. Suppose that management
training reduces 2 while leaving unchanged, and that all the firms in the cluster
eventually share the same 2.2 Before the training a large variance 2Hprevailed and
after it a low variance 2Lprevails. Before the training, the firm has a lower certainty
equivalent profit and a smaller employment size (i.e., L*H < L*L). In the long-run
equilibrium, a typical MSE owners certainty equivalent profit is driven down to zero,
because whenever it is positive, a new entrant will imitate the incumbentsbusinesses and
start its own business, increasing the total output of the cluster, Z, and lowering the output
price along the demand curve p(Z) in the local market. It is easy to show that the
long-run equilibrium employment is greater after the training than before it (i.e., n*HL*H